SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification

TL;DR

This paper introduces SELECT, a large-scale benchmark for evaluating data curation strategies in image classification, along with a new dataset, ImageNet++, to systematically compare different approaches.

Contribution

It presents the first comprehensive benchmark for data curation strategies in image classification and creates a new dataset, ImageNet++, for systematic evaluation.

Findings

Curation strategies like synthetic data and CLIP-based lookup are competitive for certain tasks.

Original ImageNet-1K curation remains the most effective strategy.

Benchmark and dataset enable systematic comparison of data curation methods.

Abstract

Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devoted towards a large-scale, systematic comparison of various curation methods. In this work, we take steps towards a formal evaluation of data curation strategies and introduce SELECT, the first large-scale benchmark of curation strategies for image classification. In order to generate baseline methods for the SELECT benchmark, we create a new dataset, ImageNet++, which constitutes the largest superset of ImageNet-1K to date. Our dataset extends ImageNet with 5 new training-data shifts, each approximately the size of ImageNet-1K itself, and each assembled using a distinct curation strategy. We evaluate our data curation baselines in two ways: (i) using each training-data shift to train identical image classification models from scratch (ii) using the data itself to fit a pretrained self-supervised representation. Our findings show interesting trends, particularly pertaining to recent methods for data curation such as synthetic data generation and lookup based on CLIP embeddings. We show that although these strategies are highly competitive for certain tasks, the curation strategy used to assemble the original ImageNet-1K dataset remains the gold standard. We anticipate that our benchmark can illuminate the path for new methods to further reduce the gap. We release our checkpoints, code, documentation, and a link to our dataset at https://github.com/jimmyxu123/SELECT.